Cardiac valve event timing plays a crucial role when conducting clinical measurements using echocardiography. However, established automated approaches are limited by the need of external electrocardiogram sensors, and manual measurements often rely on timing from different cardiac cycles. Recent methods have applied deep learning to cardiac timing, but they have mainly been restricted to only detecting two key time points, namely end-diastole (ED) and end-systole (ES). In this work, we propose a deep learning approach that leverages triplane recordings to enhance detection of valve events in echocardiography. Our method demonstrates improved performance detecting six different events, including valve events conventionally associated with ED and ES. Of all events, we achieve an average absolute frame difference (aFD) of maximum 1.4 frames (29 ms) for start of diastasis, down to 0.6 frames (12 ms) for mitral valve opening when performing a ten-fold cross-validation with test splits on triplane data from 240 patients. On an external independent test consisting of apical long-axis data from 180 other patients, the worst performing event detection had an aFD of 1.8 (30 ms). The proposed approach has the potential to significantly impact clinical practice by enabling more accurate, rapid and comprehensive event detection, leading to improved clinical measurements.

翻译：心脏瓣膜事件定时在使用超声心动图进行临床测量时至关重要。然而，现有的自动化方法因需要外部心电传感器而受到限制，且手动测量通常依赖于不同心动周期的时间点。近期研究已将深度学习应用于心脏定时，但主要局限于检测两个关键时间点，即舒张末期和收缩末期。在本工作中，我们提出一种利用三平面记录增强超声心动图中瓣膜事件检测的深度学习方法。我们的方法在检测六种不同事件（包括传统上与舒张末期和收缩末期相关的瓣膜事件）方面展现出更优性能。在所有事件中，通过十折交叉验证对240名患者的三平面数据进行测试分裂，我们实现了从舒张停顿起始的绝对帧差最大值1.4帧（29毫秒）到二尖瓣开启时的0.6帧（12毫秒）不等的检测精度。在包含另外180名患者心尖长轴数据的外部独立测试中，表现最差的事件检测绝对帧差为1.8（30毫秒）。所提出的方法有望通过实现更准确、快速和全面的事件检测，显著影响临床实践，从而改善临床测量效果。